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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

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Surface enhanced spatially offset Raman spectroscopic (SESORS) imaging - the next dimension

Stone, Nicholas and Kerssens, Marleen and Lloyd, Gavin Rhys and Faulds, Karen and Graham, Duncan and Matousek, Pavel (2011) Surface enhanced spatially offset Raman spectroscopic (SESORS) imaging - the next dimension. Chemical Science, 2 (4). pp. 776-780. ISSN 2041-6520

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Abstract

SESORS - Surface enhanced spatially offset Raman spectroscopy-imaging is explored for the first time in this study. Multiplexed surface enhanced Raman scattering (SERS) signals have been recovered non-invasively from a depth of 20 mm in tissues for the first time and reconstructed to produce a false colour image. Four unique 'flavours' of SERS nanoparticles (NPs) were injected into a 20 x 50 x 50 mm porcine tissue block at the corners of a 10 mm square. A transmission Raman data cube was acquired over an 11 x 11 pixel grid made up of 2 mm steps. The signals were reconstructed using the unique peak intensities of each of the nanoparticles. A false colour image of the relative signal levels was produced, demonstrating the capability of multiplexed imaging of SERS nanoparticles using deep Raman spectroscopy. A secondary but no less significant achievement was to demonstrate that Raman signals from SERS nanoparticles can be recovered non-invasively from samples of the order of 45-50 mm thick. This is a significant step forward in the ability to detect and identify vibrational fingerprints within tissue and offers the opportunity to adapt these particles and this approach into a clinical setting for disease diagnosis.